Autor segons l'article: Lanzarini L; Villa Monte A; Bariviera A; Jimbo Santana P
Departament: Gestió d'Empreses
Autor/s de la URV: Fernández Bariviera, Aurelio
Paraules clau: Risk Particle swarm optimization Learning vector quantization Credit risk Classification rules Classification Algorithms
Resum: One of the key elements in the banking industry relies on the appropriate selection of customers. To manage credit risk, banks dedicate special efforts to classify customers according to their risk. The usual decision-making process consists of gathering personal and financial information about the borrower. Processing this information can be time-consuming, and presents some difficulties because of the heterogeneous structure of data. This paper presents an alternative method that is able to generate rules that work not only on numerical attributes but also on nominal ones. The key feature of this method, called learning vector quantization and particle swarm optimization (LVQ+PSO), is the finding of a reduced set of classifying rules. This is possible because of the combination of a competitive neural network with an optimization technique. These rules constitute a predictive model for credit risk approval. The reduced quantity of rules makes this method useful for credit officers aiming to make decisions about granting a credit. It also could act as an orientation for borrower's self evaluation about her/his creditworthiness. This study develops a new method that combines a competitive neural network and an optimization technique. It was applied to a real database of a financial institution in a developing country.
Àrees temàtiques: Theoretical computer science Software Social sciences (miscellaneous) Medicina ii Interdisciplinar Information systems Engineering (miscellaneous) Engenharias iv Engenharias iii Electrical and electronic engineering Control and systems engineering Computer science, cybernetics Computer science (miscellaneous) Ciências biológicas i Ciências ambientais Ciência da computação Artificial intelligence Arquitetura, urbanismo e design Arquitetura e urbanismo
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: aurelio.fernandez@urv.cat
Identificador de l'autor: 0000-0003-1014-1010
Data d'alta del registre: 2024-09-07
Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
Enllaç font original: https://www.emerald.com/insight/content/doi/10.1108/K-06-2016-0158/full/html
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Kybernetes. 46 (1): 8-16
Referència de l'ítem segons les normes APA: Lanzarini L; Villa Monte A; Bariviera A; Jimbo Santana P (2017). Simplifying credit scoring rules using LVQ + PSO. Kybernetes, 46(1), 8-16. DOI: 10.1108/K-06-2016-0158
DOI de l'article: 10.1108/K-06-2016-0158
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2017
Tipus de publicació: Journal Publications